Finding Firms with Largest Delta in Net COVID Sentiment

library(tidyverse)
library(ggplot2)
library(dplyr)
library(lubridate)
library(corrr)
library(haven)

Read in data file

firms <- read_dta('../data/firmquarter_2022q1.dta')
firms 

NA

Find entries whose COVID Net Sentiment isn’t 0

NonZero_Firms <- firms |> 
      filter(`Covid_Net_Sentiment` != 0) |> 
      filter(hqcountrycode == 'US') |> 
      mutate(date_earningscall = dmy(date_earningscall)) 
      
NonZero_Firms 

Find firms (and their earnings calls) the largest Deltas in their Net COVID Sentiments over time.

Min_Max_Net_COVID_Sentiments <- NonZero_Firms |> 
                                    group_by(company_name) |> 
                                    mutate(Min_Net_COVID_Sentiment = min(Covid_Net_Sentiment)) |> 
                                    mutate(Max_Net_COVID_Sentiment = max(Covid_Net_Sentiment)) |> 
                                    mutate(Delta_Net_COVID_Sentiment = Max_Net_COVID_Sentiment - Min_Net_COVID_Sentiment) |> 
                                    arrange(desc(Delta_Net_COVID_Sentiment))
  
Min_Max_Net_COVID_Sentiments  

write_csv(Min_Max_Net_COVID_Sentiments, '../data/Min_Max_Net_COVID_Sentiments.csv')

Find top firms with the largest Deltas in their Net COVID Sentiments over time.

Top_Firms <- Min_Max_Net_COVID_Sentiments  |> 
                  distinct(company_name)  
Top_Firms

write_csv(Top_Firms, '../data/Top_Firms.csv')

Dates_Min_Max_Net_COVID_Sentiments <- Min_Max_Net_COVID_Sentiments |> 
          group_by(company_name) |> 
          filter(Covid_Net_Sentiment == Min_Net_COVID_Sentiment | Covid_Net_Sentiment == Max_Net_COVID_Sentiment) |> 
          arrange(desc(Delta_Net_COVID_Sentiment))

Dates_Min_Max_Net_COVID_Sentiments

write_csv(Dates_Min_Max_Net_COVID_Sentiments, '../data/Dates_Min_Max_Net_COVID_Sentiments.csv')

Duration Calculation: Find how long it took each firm to traverse the Delta between its lowest and highest Net COVID Sentiment:

Journey_Duration_Min_Max_Net_COVID_Sentiments <- Dates_Min_Max_Net_COVID_Sentiments |> 
                                                      group_by(company_name) |> 
                                                      mutate(Journey_Duration = interval(date_earningscall))

Error in `mutate()`:
ℹ In argument: `Journey_Duration = interval(date_earningscall)`.
ℹ In group 1: `company_name = "1-800-Flowers.Com Inc"`.
Caused by error:
! `Journey_Duration` must be size 2 or 1, not 0.
Backtrace:
 1. dplyr::mutate(...)
 8. dplyr:::dplyr_internal_error(...)

Find how long it took each firm to traverse the Delta between its lowest and highest Net COVID Sentiment:

Journey_Duration <- Min_Max_Net_COVID_Sentiments |> 
                     group_by(company_name) |> 
                     mutate(Start_Date = Min_Max_Net_COVID_Sentiments$date_earningscall where (Covid_Net_Sentiment==min(Covid_Net_Sentiment)))

Error: unexpected symbol in:
"                     group_by(company_name) |> 
                     mutate(Start_Date = Min_Max_Net_COVID_Sentiments$date_earningscall where"